Use umap-learn instead of cuml.

examples/Model use.lynxkite.json

@@ -176,7 +176,7 @@
         },
         "params": {
           "table_name": "df",
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+          "test_ratio": "0.3"
         },
         "status": "done",
         "title": "Train/test split"
@@ -575,8 +575,8 @@
                     "Input__tensor_1_output"
                   ],
                   "loss_inputs": [
-                    "Input__tensor_3_output",
-                    "Output_1_x"
+                    "Output_1_x",
+                    "Input__tensor_3_output"
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                   "outputs": [
                     "Output_1_x"
@@ -800,8 +800,8 @@
                     "Input__tensor_1_output"
                   ],
                   "loss_inputs": [
-                    "Input__tensor_3_output",
-                    "Output_1_x"
+                    "Output_1_x",
+                    "Input__tensor_3_output"
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                   "outputs": [
                     "Output_1_x"
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           "other": {
-            "model": "ModelConfig(model=Sequential(\n  (0) - Identity(): Input__tensor_1_output -> START_Repeat_1_output\n  (1) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (2) - <function leaky_relu at 0x7a324738f100>: Linear_1_output -> Activation_1_output\n  (3) - Identity(): Activation_1_output -> START_Repeat_1_output\n  (4) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (5) - <function leaky_relu at 0x7a324738f100>: Linear_1_output -> Activation_1_output\n  (6) - Identity(): Activation_1_output -> END_Repeat_1_output\n  (7) - Identity(): END_Repeat_1_output -> Output_1_x\n  (8) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Input__tensor_3_output', 'Output_1_x'], loss=Sequential(\n  (0) - <function constant_vector.<locals>.<lambda> at 0x7a3012b563e0>: nothing -> Constant_vector_1_output\n  (1) - <built-in method add of type object at 0x7a324399ef00>: Input__tensor_3_output, Constant_vector_1_output -> Add_1_output\n  (2) - <function mse_loss at 0x7a3247398c20>: Output_1_x, Add_1_output -> MSE_loss_2_output\n  (3) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.1, 'type': <OptionsFor_type.SGD: 4>}, optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n), source_workspace='Model definition.lynxkite.json', trained=True)"
+            "model": "ModelConfig(model=Sequential(\n  (0) - Identity(): Input__tensor_1_output -> START_Repeat_1_output\n  (1) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (2) - <function leaky_relu at 0x78e367f84d60>: Linear_1_output -> Activation_1_output\n  (3) - Identity(): Activation_1_output -> START_Repeat_1_output\n  (4) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (5) - <function leaky_relu at 0x78e367f84d60>: Linear_1_output -> Activation_1_output\n  (6) - Identity(): Activation_1_output -> END_Repeat_1_output\n  (7) - Identity(): END_Repeat_1_output -> Output_1_x\n  (8) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Output_1_x', 'Input__tensor_3_output'], loss=Sequential(\n  (0) - <function constant_vector.<locals>.<lambda> at 0x78e237191260>: nothing -> Constant_vector_1_output\n  (1) - <built-in method add of type object at 0x78e36059ef00>: Input__tensor_3_output, Constant_vector_1_output -> Add_1_output\n  (2) - <function mse_loss at 0x78e367f86840>: Output_1_x, Add_1_output -> MSE_loss_2_output\n  (3) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.1, 'type': <OptionsFor_type.SGD: 4>}, optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n), source_workspace='Model definition', trained=True)"
           },
           "relations": []
         },
@@ -3063,8 +3083,8 @@
                     "Input__tensor_1_output"
                   ],
                   "loss_inputs": [
-                    "Input__tensor_3_output",
-                    "Output_1_x"
+                    "Output_1_x",
+                    "Input__tensor_3_output"
                   ],
                   "outputs": [
                     "Output_1_x"
@@ -3128,67 +3148,187 @@
             {
               "data": [
                 [
-                  -1736.5657958984375,
-                  1719.049560546875,
+                  5.752160549163818,
+                  -0.8232598900794983,
                   "",
-                  -14.092952728271484
+                  -2.058953046798706
                 ],
                 [
-                  -907.293212890625,
-                  -499.864501953125,
+                  0.7012296915054321,
+                  0.5504218339920044,
                   "",
-                  -13.588829040527344
+                  4.149958610534668
                 ],
                 [
-                  261.7459716796875,
-                  1838.5341796875,
+                  2.945161819458008,
+                  -1.014856219291687,
                   "",
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+                  0.019882630556821823
                 ],
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-                  -2163.96533203125,
-                  -1887.328125,
+                  2.5901684761047363,
+                  -1.2229167222976685,
                   "",
-                  -11.786212921142578
+                  0.34879058599472046
                 ],
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-                  1305.711669921875,
-                  -779.961181640625,
+                  1.4952237606048584,
+                  -0.1513950079679489,
                   "",
-                  -13.029867172241211
+                  2.7570903301239014
                 ],
                 [
-                  2229.743408203125,
-                  -2041.06298828125,
+                  1.344888687133789,
+                  -1.1611599922180176,
                   "",
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+                  2.0714128017425537
                 ],
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-                  2131.1240234375,
-                  2083.50732421875,
+                  0.3736271560192108,
+                  0.34866970777511597,
                   "",
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+                  3.9460649490356445
                 ],
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-                  -1227.827880859375,
-                  425.2873840332031,
+                  4.606325149536133,
+                  -0.44266968965530396,
                   "",
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+                  -1.2447248697280884
                 ],
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-                  721.362548828125,
-                  242.73641967773438,
+                  0.8212581276893616,
+                  -0.6836636066436768,
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+                  5.419023513793945
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+                  3.042868137359619,
+                  -1.6362674236297607,
                   "",
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+                  0.780156135559082
+                ],
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+                  "",
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+                ],
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+                  1.2451283931732178,
+                  "",
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+                ],
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+                  "",
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+                ],
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+                ],
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+                  -1.3382771015167236,
+                  "",
+                  -3.1805598735809326
+                ],
+                [
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+                  1.0794298648834229,
+                  "",
+                  4.508198261260986
+                ],
+                [
+                  5.0630598068237305,
+                  -0.9982349276542664,
+                  "",
+                  -2.31571626663208
+                ],
+                [
+                  1.388333797454834,
+                  -0.6052800416946411,
+                  "",
+                  2.2662086486816406
+                ],
+                [
+                  5.247096061706543,
+                  -1.422383189201355,
+                  "",
+                  -2.9033005237579346
+                ],
+                [
+                  5.0349931716918945,
+                  -0.20492368936538696,
+                  "",
+                  -1.5952072143554688
+                ],
+                [
+                  0.8588991165161133,
+                  0.931562602519989,
+                  "",
+                  4.554385185241699
+                ],
+                [
+                  2.5351479053497314,
+                  -0.6277468204498291,
+                  "",
+                  -0.05971255153417587
+                ],
+                [
+                  2.1338918209075928,
+                  -0.7544606328010559,
+                  "",
+                  1.1391218900680542
+                ],
+                [
+                  5.549644470214844,
+                  -0.3897499740123749,
+                  "",
+                  -2.751723289489746
+                ],
+                [
+                  2.397034168243408,
+                  -1.7871567010879517,
+                  "",
+                  0.6067442297935486
+                ],
+                [
+                  1.2974268198013306,
+                  0.5350163578987122,
+                  "",
+                  3.7050516605377197
+                ],
+                [
+                  0.1969093680381775,
+                  1.3218902349472046,
+                  "",
+                  4.904805660247803
                 ]
               ],
-              "symbolSize": 39.81071705534973,
+              "symbolSize": 25.65378780242026,
               "type": "scatter"
             }
           ],
@@ -3215,8 +3355,8 @@
                 "#FDE725"
               ]
             },
-            "max": -11.095306396484375,
-            "min": -16.72292709350586,
+            "max": 5.419023513793945,
+            "min": -3.59246563911438,
             "right": 10,
             "top": "center"
           },
@@ -3269,8 +3409,8 @@
                     "Input__tensor_1_output"
                   ],
                   "loss_inputs": [
-                    "Input__tensor_3_output",
-                    "Output_1_x"
+                    "Output_1_x",
+                    "Input__tensor_3_output"
                   ],
                   "outputs": [
                     "Output_1_x"
@@ -3366,14 +3506,14 @@
         "title": "View vectors"
       },
       "dragHandle": ".bg-primary",
-      "height": 668.0,
+      "height": 600.0,
       "id": "View vectors 1",
       "position": {
-        "x": 3091.4609779299844,
-        "y": 659.497108066971
+        "x": 3097.090807936859,
+        "y": 623.3574330263067
       },
       "type": "visualization",
-      "width": 613.0
+      "width": 662.0
     }
   ]
 }

lynxkite-app/web/src/workspace/nodes/LynxKiteNode.tsx

@@ -76,6 +76,7 @@ function LynxKiteNodeComponent(props: LynxKiteNodeProps) {
           <>
             {data.error && <div className="error">{data.error}</div>}
             <ErrorBoundary
+              resetKeys={[props]}
               fallback={
                 <p className="error" style={{ display: "flex", alignItems: "center", gap: 8 }}>
                   <Skull style={{ fontSize: 20 }} />

lynxkite-graph-analytics/pyproject.toml

@@ -5,31 +5,38 @@ description = "The graph analytics executor and boxes for LynxKite"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-    "fsspec>=2025.2.0",
-    "grand-cypher>=0.12.0",
+    "fsspec>=2025.3.2",
+    "grand-cypher>=0.13.0",
     "joblib>=1.4.2",
     "lynxkite-core",
-    "matplotlib>=3.10.0",
+    "matplotlib>=3.10.1",
     "networkx[default]>=3.4.2",
-    "osmnx>=2.0.1",
+    "osmnx>=2.0.2",
     "pandas>=2.2.3",
-    "polars[gpu]>=1.14.0",
-    "torch>=2.6.0",
+    "polars>=1.25.2",
+    "pylibcugraph-cu12>=25.4.0",
+    "torch>=2.7.0",
     "torch-geometric>=2.6.1",
-    "cuml-cu12>=25.2.1",
+    "umap-learn>=0.5.7",
 ]
 
 [project.optional-dependencies]
 dev = [
-    "pytest>=8.3.4",
-    "pytest-asyncio>=0.25.3",
+    "pytest>=8.3.5",
+    "pytest-asyncio>=0.26.0",
 ]
 gpu = [
-    "nx-cugraph-cu12>=24.12.0",
+    "nx-cugraph-cu12>=25.4.0",
+    "polars[gpu]>=1.25.2",
 ]
 
 [tool.uv.sources]
 lynxkite-core = { path = "../lynxkite-core" }
+pylibcugraph-cu12 = { index = "nvidia" }
 
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
+
+[[tool.uv.index]]
+name = "nvidia"
+url = "https://pypi.nvidia.com"

lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py

@@ -8,7 +8,7 @@ from lynxkite.core import workspace
 from .pytorch import pytorch_core
 from lynxkite.core import ops
 from tqdm import tqdm
-import cuml
+import umap
 import joblib
 import pandas as pd
 import pathlib
@@ -179,14 +179,14 @@ def view_vectors(
     metric: UMAPMetric = UMAPMetric.euclidean,
 ):
     vec = np.stack(bundle.dfs[table_name][vector_column].to_numpy())
-    umap = functools.partial(
-        cuml.manifold.umap.UMAP,
+    _umap = functools.partial(
+        umap.UMAP,
         n_neighbors=n_neighbors,
         min_dist=min_dist,
         metric=metric.value,
     )
-    proj = umap(n_components=2).fit_transform(vec)
-    color = umap(n_components=1).fit_transform(vec)
+    proj = _umap(n_components=2).fit_transform(vec)
+    color = _umap(n_components=1).fit_transform(vec)
     data = [[*p.tolist(), "", c.item()] for p, c in zip(proj, color)]
     if label_column:
         for i, row in enumerate(bundle.dfs[table_name][label_column]):